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Standard supervised learning frameworks for image restoration require a set of noisy measurement and clean image pairs for which a distance between the output of the restoration model and the ground truth images is minimized. The ground truth images, however, are often unavailable or very expensive to acquire in real-world applications. We circumvent this problem by proposing a class of structured denoisers that can be decomposed as the sum of a nonlinear image-dependent mapping, a lineardoi:10.17863/cam.68734 fatcat:siulqsib2jh3hdo32rntgshd6a